Murray Bruce Reed

The paper investigates the cellular and mitochondrial factors influencing regional glucose metabolism in the human cortex using multimodal data integration.

The paper presents an open-access framework for PET attenuation correction using pseudo-CT methods with a database of MRI-CT scans, showing that UCL achieves the lowest quantification error.

The paper examines network-specific metabolic costs of functional connectivity in the human brain using PET/MR data, revealing differences in metabolic demand across brain networks and changes in Alzheimer's disease.

This study examines how acute psychosocial stress reconfigures brain metabolism and network organization using PET/MRI, revealing changes in the default mode network and its interactions with other networks.

This paper develops a noise correction method for high-temporal resolution fPET data, showing it improves metabolic connectivity estimation across different scanners and frequency bands.

This paper compares molecular connectivity (MC) and covariance (mCov) methods using fPET data to assess metabolic associations between brain regions, finding that different methods perform better at varying temporal resolutions.

The paper introduces a unified fPET toolbox for analyzing neuronal activation and molecular connectivity, validated across cognitive tasks and radiotracers.

This study evaluates motion correction techniques for high temporal resolution fPET and finds that neither deep learning nor sliding window averaging significantly improves motion artifact mitigation compared to standard methods.

This study evaluates filtering techniques for fPET data, finding that dNLM and MRI-MRF filters improve signal-to-noise ratio and test-retest reliability, with dNLM reducing sample size needs.

This study shows that incorrect baseline modeling in fPET-FDG can create false metabolic activity patterns, affecting GLM analyses.

The paper introduces a standardized nomenclature for PET-based molecular connectivity and covariance to improve reproducibility and interpretation across imaging modalities and radiotracers.

This paper introduces a method to assess cerebral glucose metabolism changes during tasks without blood sampling, using GLM beta values and percent signal change, showing strong correlation with standard CMRGlu measurements.

This study uses high-temporal resolution PET/MRI to show synchronized metabolic and hemodynamic brain responses during working memory tasks.

The paper examines the correlation between mRNA expression and receptor density in the human cerebral cortex using autoradiography and transcriptomic data from multiple sources.

This study investigates escitalopram's effect on white matter plasticity during learning and relearning using diffusion tensor imaging in healthy individuals, finding no significant changes in DTI metrics.

The paper introduces a novel fPET approach to assess dopamine synthesis during reward and punishment processing, revealing sex-specific differences in dopamine response to monetary gains and losses.

This study examines the effects of escitalopram and relearning on grey matter in healthy humans but finds no significant effect of the SSRI on structural neuroplasticity during relearning.

The paper identifies cortical areas with enriched disease-associated genes using transcriptome-based parcellation and GWAS data, revealing potential roles in psychiatric and neurological disorders.

This study shows that escitalopram modulates functional brain network connectivity during relearning, with content-specific effects on neuroplasticity.